The history of the optical high resolution satellite images starts from classified military satellite systems of the United States of America that captured earth’s surface from 1960 to 1972. All these images were declassified by Executive Order 12951 in 1995 and made publically available (Now freely available through the USGS EarthExplorer data platform under the category of declassified data). From 1999 onward, commercial multispectral and panchromatic datasets have been available for public. Launch of Keyhole Earthviewer in 2001, later renamed as Google Earth in 2005, opened a new avenue for the layman to visualize earth features through optical high resolution satellite images.

In the current era, most high resolution satellite images are commercially available, and are being used as a substitute to aerial photographs. The launch of SPOT, IKONOS, QuickBird, OrbView, GeoEye, WorldView, KOMPSAT etc. offer data at fine resolutions in digital format to produce maps in much simpler, cost effective and efficient manner in terms of mathematical modeling. A number of meaningful products are being derived from high resolution datasets, e.g., extraction of high resolution Digital Elevation Models (DEMs) with 3D building models, detailed change assessments of land cover and land use, habitat suitability, biophysical parameters of trees, detailed assessments of pre and post-disaster conditions, among others.

Both aerial photographs and high resolution images are subject to weather conditions but satellites offer the advantage of repeatedly capturing same areas on a reliable basis by considering the user demand without being restricted by considering borders and logistics, as compared to aerial survey.

Pansharpening / resolution merge provides improved visualization and is also used for detecting certain features in a better manner. Pansharpening / resolution merge is a fusion process of co-georegistered panchromatic (high resolution) and multispectral (comparatively lower resolution) satellite data to produce high-resolution color multispectral image. In high resolution satellite data, the spectral resolution is being increased and more such sensors with enhanced spectral sensitivity are being planned in the future.

List of the Spaceborne Sensors with <5 m Spatial Resolution

Sensors

Agency/Country

Launch Date

Platform altitude (km)

GSD Pan/MSS (m)

Pointing capability (o)

Swath width at nadir (km)

IKONOS-2

GeoEye Inc./USA

1999

681

0.82/3.2

Free View

11.3

EROS A1

ImageSat Int./Cyprus (Israel)

2000

480

1.8

Free View

12.6

QuickBird

DigitalGlobe/USA

2001

450

0.61/2.44 Pan and MSS alternative

Free View

16.5

HRS

SPOT Image/France

2002

830

5X10

Forward/left +20/-20

120

HRG

SPOT Image/France

2002

830

5(2.5)x10

sideways up to ±27

60

OrbViw-3

GeoEye Inc./USA

2003

470

1/4

Free View

8

FORMOSAT 2

NSPO/China, Taiwan

2004

890

2/8

Free View

24

PAN (Cartosat-1)

ISRO/India

2005

613

2.5

Forward/aft 26/5 Free view to side up to 23

27

TopSat Telescope

BNSC/UK

2005

686

2.8/5.6

Free View

15/10

PRISM

JAXA/Japan

2005

699

2.5

Forward/Nadir/aft -24/0/+24 Free view to side

70 35 (Triplet stereo observations

PAN(BJ-1)

NRSCC (CAST)/China

2005

686

4/32

Free View

24/640

EROS B

ImageSat Int./Cyprus (Israel)

2006

508

0.7/-

Free View

7

Geoton-L1Resurs-DK1

Roscosmos/Russia

2006

330-585

1/3 for h = 330km

Free View

30 for h = 330km

KOMPSAT-2

KARI/South Korea

2006

685

1/4

sideways up to ±30

15 km

CBERS-2B

CNSA/INPE China/Brazil

2007

778

2.4/20

Free View

27/113

WorldView-1

DigitalGlobe/USA

2007

494

0.45/-

Free View

17.6

THEOS

GISTDA/Thailand

2008

822

2/15

Free View

22/90

AlSat-2

Algeria

2008

680

2.5

up to 30 cross track Free view

17.5

GeoEye-1

GeoEye Inc./USA

2008

681

0.41/1.65

Free View

15.2

WorldView-2

DigitalGlobe/USA

2009

770

0.45/1.8

Free View

16.4

PAN (Cartosat-2, 2A, 2B)

ISRO/India

Cartosat 2-2007 Cartosat 2A-2008 Cartosat 2B-2010

631

0.82/-

Free View

9.6

KOMPSAT-3

KARI/South Korea

2012

685

0.7/2.8

±45º into any direction (cross-track or along-track)

15

WorldView-3

DigitalGlobe/USA

2014

617

0.3/1.24/3.7/30

13.1

Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here.

Very good blog post on ForestPlanet about the future of EO data, portals, and platforms. I also foresee near-real-time satellite imagery becoming available to the user community within the next 5-6 years. The boom of small-satellite constellations in both the optical (e.g. Planet) and SAR (e.g. Capella Space, IceEye) is already a game changer in the industry, and will cause more disruption in the EO data + analytics domains in the future.

The adversaries are the Earth observation platform providers. The battle ground is “content”.

The notion of “content” shaping the market success of new technologies is not new. In the 1970s the range of movies available on VHS helped it beat Betamax, and when several film studios committed to Blu-ray in 2008, it was the death nell for HD DVD. Today, similar content battles are being fought between Netflix and Disney, or Xbox and Play Station. “Our future largely lies in exclusive original content,” said Netflix in its latest earnings report.

The same is true for the geospatial platform providers. The “content” is geospatial data, fuelled by the explosion in new commercial Earth observation satellites. This is also the inspiration for predictions that the global remote sensing services market will double over the next 5 years from $10.68 billion to…

NetCDF files are a common format for distributing Earth Observation data and allow the ability to store a number of variables alongside metadata. However, using netCDF files in a GIS is not always as easy as it could be.

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) routinely produce products such as Chlorophyll from EO data and store as netCDF files. For the UK they use a Mercator projection within a netCDF file storing the latitude and longitude of each pixel within separate arrays. Unfortunately QGIS and ArcMap are often unable to read this information so don’t read data into the correct location making it difficult to use with other datasets.

To read data into the correct location I wrote a script which converts the latitude and longitude values in the netCDF file into tie points and then uses these to warp a GeoTiff into the…

Recently, the General Election 2018 was held in Pakistan. I was following with interest some of the mapping and visualization tools developed and used by different media outlets for communicating and consolidating the election results. Below I give a brief overview of the different visualizations used. Please note that these are just my personal views, and I am not a “GIS expert” per se to really comment on the technical tools and implementation. I am just giving my opinion from a user perspective and an EO scientist / professional.

This was developed by DAWN GIS team and TPL Maps. I liked the layout and the map design; the map information is very detailed at different zoom levels. The idea of displaying election constituency labels by hovering over the areas was very nice. However, I feel that the map was not utilized to its full capacity. As a EO professional, I would have loved if DAWN would have used this map as a focus point for their election coverage, but instead it was relegated a bit to the side. Also, as you can see now, none of the election results are updated / available on this map, and it only shows the major candidates for each constituency. However, at the same time, they show the past election results in a nice graphical format, which was a good idea. The search functionality is also good.

The Geo TV map-like output is good in that way it immediately lets the user see which party won which constituency through the color-coding. However, there is no color legend, not even in the table below showing the overall election results. Yes, there is a little bit color coding when we open the results from the top bar. But the effort needed a lot of contribution from a user-interface designer or someone similar. Also, I am not sure how much true “GIS” was used here. A good feature is when you click “details” it takes the user to another page with lot of details about the constituency.

This map also uses the hover labeling as in the DAWN map; however the DAWN map hover is better as it also auto-highlights the constituency to give a better interactive response to the user. Furthermore, the map frame size is too small; would have been better to use the full page width. Both 2013 and 2018 election results are updated. The search functionality is useful. The color legend makes the map very user-friendly.

A unique and very interesting visualization by Plotree. I don’t know how much actual “GIS” is used here, for example there are no constituency boundaries, but perhaps this is a decision by the developers to not clutter with too much information. There are many unique and interesting visualisation features here, such as: the circle size shows vote margin for winner, just hovering over the circle shows succinct summary of voting details, using the filter can immediately show location-wise which parties have won more. None of the other maps have given a map for provincial elections, but Plotree maps give results of each province as well. There is also the District Wise Vote Share feature, which I invite the readers to explore themselves.

A few weeks ago, I had written some bash code in Max OSX terminal to identify the current date, and then defining the date a few days back in time. When I tried to run the same code in Ubuntu bash terminal, the code line for identifying the previous date fails. Some brief time spent on Google told me that there is some difference in how the “date” function is implemented in Mac OSX and Ubuntu bash. The correct usage is as follows:

macOS Sierra comes with a built-in default Python installation. On macOS Sierra 10.12.6, this default installation is on the /System/Library/Frameworks folder (which, by the way, is a critical system folder and should not be touched). macOS Sierra 10.12.6 comes with Python 2.7, which is getting outdated very fast, and also Apple itself recommends officially that to run a coding project, users should install their own updated version of Python with their own dependencies setup.

I had installed Python 2.7 myself last year when I was in a mood to start working on Python, and at that time I didn’t know that Python comes installed in macOS by default. Now I have to start working with Python in earnest for a project, and wanted to uninstall the custom Python 2.7 installation, so that I can start from scratch on a new installation of Python 3. This required some web-surfing, and some hours to figure out how to do it properly. After reading and deciphering some posts by others, I can now give an updated clean solution.

This assumes you have a good knowledge of shell / bash usage. This works for Python 2.7 installed by the user, but I am sure it works the same if you want to uninstall Python 3 from macOS Sierra too.

Step 4: Clear symbolic links to deleted Python files. If you have Homebrew installed already (highly recommended), then simply run brew doctor first, which will show you the broken symbolic links. Then just run brew prune to fix them (you can check it by running brew doctor again). If you don’t have Homebrew installed, then follow Step 3 here.

NOTE: After uninstallation, I do need to fix the system to call the default python version installed in macOS Sierra. Probably need to revise some path specifications. But I am more concerned with the new Python3 installation at this point :). See here for more on this.

CAUTION: Under no circumstances should you try to delete or touch anything in the /System or /usr/bin/python folders. This can cause your macOS to malfunction, your Macbook could self-destruct, and there is a possibility of an alien invasion as well. If you don’t believe me, just do a web search on why not to touch anything in the macOS /System folder.

I just discovered this amazing Synthetic Aperture Radar (SAR) website and magazine site, so aptly named as www.syntheticapertureradar.com. The website and content in it is quite amazing, and being a SAR aficionado, I have immediately signed up for their newsletter. I wish someone sends me an invite to the “Community” also, it seems to be only by invitation 🙂

In their own words, the website managers “represent the worldwide airborne and spaceborne SAR community worldwide. We are operated, moderated and maintained by members of the SAR community.”

So take a look at the SAR Journal website and sign up for the newsletter: